2018/02/27 by Fabio Garofalo, Garofalo, Fabio
Chemistry · Engineering · #Data Analysis #Electrical and Bioimpedance Tomography #Electrostatics and Colloid Interactions #FOS: Physical sciences #Microfluidic and Bio-sensing Technologies #Molecular Communication and Nanonetworks #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1802.09790
openalex publication_date 2018/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A method for the quantification of acoustophoretic separation and dispersion\nfor microparticle populations featuring continuously distributed physical\nparameters is presented. The derivation of the method starts by (i)~considering\nthe equation of motion for a particle ensemble in the coordinate+parameter\nspace, (ii)~performing moment analysis on the transport equation for the\nprobability density function (PDF), and (iii)~expanding up to the first-order\nthe drift (and the diffusion coefficient) around the mean of the PDF. Following\nthese steps, a system of ordinary differential equations for the evolution of\nthe mean and the covariance in the coordinate+parameter space is derived. These\ndifferential equations enable for the approximation of the acoustophoretic\nseparation dynamics of particle ensembles by using a gaussian mixture for which\nthe mean and the covariance of each gaussian evolve according to the\nmean-and-covariance dynamics. The approximation property of this method is\nshown by comparison with direct numerical simulations of particle ensembles in\nthe cases of prototypical models of acoustophoretic and free-flow\nacoustophoretic separations for which the particle populations are distributed\naccording to the radius. Furthermore, the indicators for quantifying free-flow\nacoustophoretic separation performance are introduced, and a method for the\ninference of particle-histogram parameters is illustrated.\n